app.py

from transformers import pipeline
import gradio as gr
from PIL import Image

# Initialize the image classification pipeline with the specific model
pipe = pipeline("image-classification", model="prithivMLmods/Age-Classification-SigLIP2")

# Prediction function
def predict(input_img):
    # Get the predictions from the pipeline
    predictions = pipe(input_img)
    
    result = {p["label"]: p["score"] for p in predictions}
    
    # Return the image and the top predictions as a string
    top_labels = [f"{label}: {score:.2f}" for label, score in result.items()]
    return input_img, "\n".join(top_labels)

# Create the Gradio interface
gradio_app = gr.Interface(
    fn=predict,
    inputs=gr.Image(label="Select Image", sources=['upload', 'webcam'], type="pil"),
    outputs=[
        gr.Image(label="Processed Image"),
        gr.Textbox(label="Result", placeholder="Top predictions here")
    ],
    title="Age Classification",
    description="Upload or capture an image to classify age using the SigLIP2 model."
)

# Launch the app
gradio_app.launch()